Unpacking Contextual Parameters Influencing the Quality of Personalized Adaptive Learning EdTech Applications

Abstract

Personalized Adaptive Learning (PAL) EdTech products are specialized applications geared towards fostering self-regulated learning of individual students. The design of these applications incorporates specific features to afford such kinds of interaction. As such, the quality of the applications is judged by the presence or absence of such specific features. However, such an approach towards designing and evaluating PAL applications is unlikely to attend to contextual parameters on the ground that may influence students’ perception of the quality of the application. Large scale user studies previously done using such applications typically focus on infrastructural capabilities needed to deploy the application on the ground or the learning outcomes for a large set of users because of using the application. Such studies overlook the need to understand why certain applications are well-received by students and hence may lead to significant learning gains while others are not viewed favorably. Moreover, such an approach to designing and evaluating PAL applications and lack of such user studies likely lead to the creation of contextually insensitive applications that do not appeal to the intended students. This study aims to unpack the contextual parameters that might affect a PAL application’s quality when used in the classroom. We use a design-based implementation research (DBIR) approach since it enables researchers and practitioners to work closely with each other and understand the nuances of application implementation on the ground, inform generation of local theories, and work towards a sustainable change on the ground. We present six contextual parameters from Indian classrooms that are likely to influence the quality of ‘PAL at school’ EdTech applications. These findings advance our knowledge of both classroom learning and implementation of PAL applications, and inform the design of contextually sensitive applications.